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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does ETL stand for in data processing?
π‘ Hint: Think about the process of moving data.
Question 2
Easy
Name a key feature of Apache Kafka.
π‘ Hint: Consider its performance in handling messages.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary purpose of MapReduce?
π‘ Hint: Focus on its main application area.
Question 2
True or False: Apache Kafka ensures message ordering across all partitions.
π‘ Hint: Think about how Kafka organizes messages.
Solve 2 more questions and get performance evaluation
Push your limits with challenges.
Question 1
Design an ETL pipeline using Apache Kafka as the core. Explain how you would handle fault tolerance and data durability.
π‘ Hint: Consider how Kafkaβs architecture supports multiple use cases.
Question 2
Compare the performance implications of using MapReduce versus Spark for a real-time analytics task. What factors should be taken into account?
π‘ Hint: Think about how speed and data retrieval methods affect performance.
Challenge and get performance evaluation